Type
E-commerce Recommendation System
Objective
A leading e-commerce platform approached Adorebits to design and develop a personalized recommendation system that would improve the shopping experience of their users. The aim was to create a system able to analyze vast data about users, predict preferences, and recommend products based on behavior from individual customers.
Technologies
React.js, Node.js, Python, SQL, Big Data, Spark, AWS, Docker, Jenkins, Kubernetes
Country
Netherland
Project Attributes
Engagement Model
Dedicated Team Engagement
Duration
13 months
App Users
E-commerce customers, product managers, and marketing teams
Challenges
- Big amounts of data: The client required the processing of user data that
consisted of daily events such as browsing history, purchases, and product views
in real time. - Personalized recommendations: The client needed experiences crafted to
enhance conversion rates. - Real-Time Processing: The real-time data of recommendations of products
created significant pressure on the existing infrastructure. - Scalability Issues: International expansion created consistent demand from
different regions to sustain the performance standards of the platform. - Marketing Integration: The system required smooth integration with targeted
marketing initiatives including email campaigns and product recommendations.
Solutions
- Front-End Development by React.js and Node.js: Adorebits used React.js to
create a dynamic user interface for products, real-time recommendations, and
Node.js to engage in the back-end for API requests, ensuring the free flow of
data across the UI and the back-end system. - Advanced Data Processing with Python, Big Data, and Spark: AI-driven
models that analyze customer behavior and purchase history are implemented
into Python scripts at Adorebits. Such models ran on Big Data’s platforms using
Spark to manage large-scale data processing for generating real-time
recommendations. - SQL utilized for real-time data management: The system was based on
optimized SQL databases for fast access and update, thereby storing user
information, product details, and behavior analysis. - Scalable Cloud Infrastructure on AWS: As AWS was the selected cloud
infrastructure, the system was highly scalable to reach millions of users
worldwide across diversified geographies with high availability and efficient load
balancing. - Containerization Using Docker and Orchestration Using Kubernetes: This
application was, therefore, containerized using Docker to make deployment
possible across several environments. Orchestration was achieved by the use of
Kubernetes, where automatic scaling can be ensured in case there are
possibilities of traffic spikes due to peak shopping seasons. - Continuous Integration with Jenkins: Jenkins was placed into our
development pipeline to ensure easy, seamless integration of new features and
updates.
Results:
- Improved Recommendation Accuracy: AI models enabled with Python and Spark showed improved accuracy levels for product suggestions
- Reduced Latency in Recommendations :The new system reduced the latency of processing data and delivering personalized product recommendations to better customer satisfaction factors.
- Increased Scalability: This facility with the cloud infrastructure of AWS in conjunction with the orchestration of Kubernetes easily and smoothly allowed for scaling up of the system.
- Cost-Effectiveness of Data Processing: The adoption of Spark and Big Data platforms led to an aggregate saving of 20% of the processing and handling user data by the client.
- Improved Customer Retention: The integrated marketing campaigns and product recommendations made users’ suggestions more relevant and valuable.
Conclusion:
Adorebits’ E-commerce Recommendation System drove further revenue growth, providing more innovative, AI-driven recommendations for a heightened level of customer satisfaction.